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Identifying In Vitro Cultured Human Hepatocytes Markers with Machine Learning Methods Based on Single-Cell RNA-Seq
ZhanDong Li1, FeiMing Huang2, Lei Chen3
1College of Biological and Food Engineering, Jilin Engineering Normal University, Changchun, China.
Frontiers in Bioengineering and Biotechnology
|June 16, 2022
Summary
This study identifies key genes like CD147 and PPIA involved in hepatocyte differentiation in vitro. Computational analysis provides targets for improving cell transplantation therapies for liver disease.
Area of Science:
- Hepatology
- Computational Biology
- Genomics
Background:
- Cell transplantation is vital for liver function but faces challenges due to hepatocyte heterogeneity.
- Understanding in vitro hepatocyte differentiation is crucial for effective transplantation.
Purpose of the Study:
- To computationally analyze single-cell transcriptional profiling of hepatocytes.
- To resolve heterogeneity in in vitro hepatocyte differentiation.
- To identify biomarkers for different differentiation stages.
Main Methods:
- Employed Boruta and Max-Relevance/Min-Redundancy methods for feature selection.
- Utilized random forest, k-nearest neighbor, and decision tree algorithms.
- Applied incremental feature selection to optimize classifiers and feature subsets.
Main Results:
- Identified key genes including CD147, PPIA, TMSB10, TMEM176B, and CD63.
- Developed a random forest classifier with a 0.940 Matthews correlation coefficient to distinguish hepatic cell types.
- Established classification rules for quantitative description of hepatic cell types.
Conclusions:
- Elucidated the qualitative and quantitative aspects of hepatocyte development in vitro.
- Provided potential molecular targets for cell transplantation strategies in liver disease.
- Highlighted the role of identified genes in regulating hepatocyte differentiation and function.
